TYPICAL DISTANCES IN THE DIRECTED CONFIGURATION MODEL

• 成果类型: Article
• 署名作者: van der Hoorn, Pim; Olvera-Cravioto, Mariana
• 署名单位: Northeastern University; University of California System; University of California Berkeley
• 刊物名称: ANNALS OF APPLIED PROBABILITY
• ISSN/ISSBN: 1050-5164
• DOI: 10.1214/17-AAP1342
• 发表日期: 2018
• 页码: 1739-1792
• 关键词: Random graphs

摘要

We analyze the distribution of the distance between two nodes, sampled uniformly at random, in digraphs generated via the directed configuration model, in the supercritical regime. Under the assumption that the covariance between the in-degree and out-degree is finite, we show that the distance grows logarithmically in the size of the graph. In contrast with the undirected case, this can happen even when the variance of the degrees is infinite. The main tool in the analysis is a new coupling between a breadth-first graph exploration process and a suitable branching process based on the Kantorovich-Rubinstein metric. This coupling holds uniformly for a much larger number of steps in the exploration process than existing ones, and is therefore of independent interest.